FMCW radar with interference signal suppression using artificial neural network

ABSTRACT

A method for a radar device is described below. According to an example implementation, the method comprises transmitting an RF transmission signal that comprises a plurality of frequency-modulated chirps, and receiving an RF radar signal and generating a dataset containing in each case a particular number of digital values based on the received RF radar signal. A dataset may in this case be associated with a chirp or a sequence of successive chirps. The method furthermore comprises filtering the dataset by way of a neural network to which the dataset is fed in order to reduce an interfering signal contained therein. A convolutional neural network is used as the neural network.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to German Patent Application No.102019106529.1 filed on Mar. 14, 2019, the content of which isincorporated by reference herein in its entirety.

TECHNICAL FIELD

The present description relates generally to the field of radar sensors,and signal processing methods used in radar sensors, which make itpossible to suppress disruptive interference.

BACKGROUND

Radar sensors are used in a number of applications to detect objects,wherein the detection usually comprises measuring distances and speedsof the detected objects. In particular in the automotive sector, thereis an increasing need for radar sensors that are able to be used, interalia, in driving assistance systems (advanced driver assistance systems,ADAS), such as for example in adaptive cruise control (ACC) or radarcruise control systems. Such systems are automatically able to adjustthe speed of a motor vehicle, in order thereby to maintain a safedistance from other motor vehicles traveling in front (and from otherobjects and from pedestrians). Further applications in the automotivesector are for example blind spot detection, lane change assist and thelike. In the field of autonomous driving, radar sensors and systemshaving a plurality of sensors will play an important role in controllingautonomous vehicles.

Since automobiles are increasingly equipped with radar sensors, theprobability of interference increases. That is to say, a radar signalemitted by a first radar sensor (installed in a first vehicle) mayspread into the reception antenna of a second radar sensor (installed ina second vehicle). The first radar signal may interfere with an echo ofthe second radar signal in the second radar sensor and thereby impairthe operation of the second radar sensor.

SUMMARY

A method for a radar device is described below. According to one exampleimplementation, the method comprises transmitting a radio-frequency (RF)transmission signal that comprises a plurality of frequency-modulatedchirps, and receiving an RF radar signal and generating a datasetcontaining in each case a particular number of digital values based onthe received RF radar signal. A dataset may in this case be associatedwith a chirp or a sequence of successive chirps. The method furthermorecomprises filtering the dataset by way of a neural network to which thedataset is fed in order to reduce an interfering signal containedtherein. A convolutional neural network is used as neural network.

A further example implementation relates to a radar device having aradar transmitter and a radar receiver. The radar transmitter isdesigned to output an RF transmission signal that comprises a pluralityof frequency-modulated chirps. The radar receiver is designed to receivean RF radar signal and, based thereon, to generate a dataset containingin each case a particular number of digital values. A dataset may inthis case be associated with a chirp or a sequence of successive chirps.The radar device furthermore comprises a neural network to which thedataset is fed and that is designed to filter the dataset in order toreduce an interfering signal contained therein. A convolutional neuralnetwork is used as neural network.

According to a further example implementation, the radar devicecomprises a radar receiver that is designed to receive an RF radarsignal and, based thereon, to generate a digital signal that comprises aplurality of signal segments. The radar device furthermore comprises aneural network having a plurality of layers each having one or moreneurons, wherein the signal segments are fed to an input layer of theplurality of layers and wherein the plurality of layers are designed toprocess the signal segments of the digital signal. An output layer ofthe plurality of layers has at least one neuron that delivers an outputvalue that indicates whether a respective signal segment or a sample,able to be associated with the neuron, of the signal segment is overlaidwith an interfering signal.

BRIEF DESCRIPTION OF THE DRAWINGS

Example implementations are explained in more detail below withreference to drawings. The illustrations are not necessarily true toscale, and the example implementations are not restricted just to theaspects that are illustrated. Rather, value is placed on illustratingthe principles underlying the example implementations. In the drawings:

FIG. 1 is a sketch for illustrating functions of an FMCW radar systemfor distance and/or speed measurement.

FIG. 2 comprises two timing diagrams for illustrating the frequencymodulation (FM) of the RF signal generated by the FMCW system.

FIG. 3 is a block diagram for illustrating the structure of an FMCWradar system.

FIG. 4 is a sketch for illustrating an example of how interferingsignals may be spread into the reception antenna of a radar sensor.

FIG. 5 is a circuit diagram for illustrating a simplified example of aradar transceiver and a further radar transceiver that causesinterference.

FIG. 6 shows a timing diagram (frequency over time) of an example of anemitted radar signal containing a plurality of sequences of chirps,wherein each sequence has a particular number of chirps that are usedfor a measurement.

FIG. 7 shows a timing diagram of a transmission signal of a radar sensorand of a transmission signal (interfering signal), causing interference,of a further radar sensor (interferer), wherein the signal profiles(frequency over time) of these signals partly overlap.

FIG. 8 shows a timing diagram of an example signal profile of a radarsignal (after mixing into baseband) that contains a radar echo from aradar target and an interfering signal (interference).

FIG. 9 illustrates, by way of example, the digital signal processing ofradar signals in range Doppler analysis.

FIGS. 10A-10D illustrate various example structures ofsuppressing/filtering or detecting interference-induced (and other)disturbances using artificial neural networks.

FIG. 11 illustrates a first example according to which individualsamples of a received radar signal that are disturbed by interferenceare detected by way of a neural network.

FIG. 12 illustrates a further example according to which signal segmentsof a received radar signal that are disturbed by interference arefiltered by way of a neural network in order to reduce the interferencecontained in the respective signal segment.

FIG. 13 illustrates a further example according to which signal segmentsof a received radar signal that are disturbed by interference aredetected by way of a neural network.

FIG. 14 illustrates a further example for filtering (denoising) a rangemap or a range Doppler map by way of convolutional neural networks(CNNs).

FIG. 15 illustrates an example implementation of the concept from FIG.14 in more detail.

FIG. 16 illustrates, by way of example, a signal processing structurehaving a convolutional neural network (CNN) for filtering the rangeDoppler maps of a radar system and the subsequent target detection.

DETAILED DESCRIPTION

FIG. 1 illustrates, in a schematic diagram, the application of afrequency-modulated continuous-wave radar system—usually referred to asFMCW radar system—in the form of a sensor for measuring distances andspeeds of objects, which are usually referred to as radar targets. Inthe present example, the radar device 1 has separate transmission (TX)and reception (RX) antennas 5 and 6, respectively (bistatic orpseudo-monostatic radar configuration). It is however noted that asingle antenna may also be used that serves simultaneously astransmission antenna and as reception antenna (monostatic radarconfiguration). The transmission antenna 5 emits a continuous RF signals_(RF)(t), which is frequency-modulated for example with a type ofsawtooth signal (periodic linear frequency ramp). The emitted signals_(RF)(t) is backscattered at the radar target T and thebackscattered/reflected signal y_(RF)(t) (echo signal) is received bythe reception antenna 6. FIG. 1 shows a simplified example; in practice,radar sensors are systems with a plurality of transmission (TX) andreception (RX) channels in order also to be able to determine thedirection of arrival (DoA) of the backscattered/reflected signaly_(RF)(t) and thus locate the radar target T with greater accuracy.

FIG. 2 illustrates, by way of example, the frequency modulation of thesignal s_(RF)(t). As illustrated in FIG. 2 (top graph), the emitted RFsignal s_(RF)(t) is composed of a series of “chirps”, that is to say thesignal s_(RF)(t) comprises a sequence of sinusoidal signal profiles(waveforms) with rising frequency (up-chirp) or falling frequency(down-chirp). In the present example, the instantaneous frequencyf_(LO)(t) of a chirp increases linearly, starting at a start frequencyf_(START), to a stop frequency f_(STOP) within a time interval T_(RAMP)(see bottom graph in FIG. 2 ). Such chirps are also referred to aslinear frequency ramps. FIG. 2 illustrates three identical linearfrequency ramps. It is however noted that the parameters f_(START),f_(STOP), T_(RAMP) and the pause between the individual frequency rampsmay vary. The frequency variation also does not necessarily have to belinear (linear chirp). Depending on the implementation, transmissionsignals with exponential or hyperbolic frequency variation (exponentialor hyperbolic chirps) may also be used, for example. For a measurement,a sequence of frequency ramps is emitted and the resulting echo signalis evaluated in baseband in order to detect one or more radar targets.

FIG. 3 is a block diagram that illustrates, by way of example, onepossible structure of a radar device 1 (radar sensor). Accordingly, atleast one transmission antenna 5 (TX antenna) and at least one receptionantenna 6 (RX antenna) are connected to an RF front end 10 integratedinto a chip, which front end may contain all those circuit componentsthat are used for RF signal processing. These circuit componentscomprise for example a local oscillator (LO), RF power amplifiers, lownoise amplifiers (LNA), directional couplers (for example rat-racecouplers, circulators, etc.), and mixers for downmixing (ordown-converting) the RF signals into baseband or an intermediatefrequency band (IF band). The RF front end 10 may—possibly together withfurther circuit components—be integrated into a chip, which is usuallyreferred to as a monolithic microwave integrated circuit (MMIC). The IFband is sometimes also referred to as baseband. No further distinctionis drawn below between baseband and IF band, and only the term basebandis used. Baseband signals are those signals on the basis of which radartargets are detected.

The example illustrated shows a bistatic (or pseudo-monostatic) radarsystem with separate RX and TX antennas. In the case of a monostaticradar system, the same antenna would be used both to emit and to receivethe electromagnetic (radar) signals. In this case, a directional coupler(for example a circulator) may for example be used to separate the RFsignals to be emitted from the received RF signals (radar echo signals).As mentioned, radar systems in practice usually have a plurality oftransmission and reception channels with a plurality of transmission andreception antennas (antenna arrays), which makes it possible, interalia, to measure the direction (DoA) from which the radar echoes arereceived. In the case of such MIMO systems (MIMO=multiple-inputmultiple-output), the individual TX channels and RX channels are usuallyin each case constructed identically or similarly and may be distributedover a plurality of integrated circuits (MMICs).

In the case of an FMCW radar system, the RF signals emitted by the TXantenna 5 may be for example in the range of approximately 20 GHz to 100GHz (for example in the range of approximately 76-81 GHz in someapplications). As mentioned, the RF signal received by the RX antenna 6contains the radar echoes (chirp echo signals), that is to say thosesignal components that are backscattered at one or at a plurality ofradar targets. The received RF signal y_(RF)(t) is downmixed for exampleinto baseband and processed further in baseband by way of analog signalprocessing (see FIG. 3 , analog baseband signal processing chain 20).The analog signal processing essentially comprises filtering andpossibly amplifying the baseband signal. The baseband signal is finallydigitized (see FIG. 3 , analog-to-digital converter 30) and processedfurther in the digital domain. The digital signal processing chain maybe implemented at least partly in the form of software that is able tobe executed on a processor, for example a microcontroller or a digitalsignal processor (see FIG. 3 , computing unit 40). The overall system isgenerally controlled by way of a system controller 50 that may likewisebe implemented at least partly in the form of software that is executedon a processor, such as for example a microcontroller. The RF front end10 and the analog baseband signal processing chain 20 (optionally alsothe analog-to-digital converter 30 and the computing unit 40) may beintegrated together in a single MMIC (that is to say an RF semiconductorchip). As an alternative, the individual components may also bedistributed over a plurality of MMICs. The computing unit 40 or partsthereof may be contained in the system controller 50.

FIG. 4 illustrates a simple example for illustrating how an interferermay interfere with the received radar echoes. FIG. 4 illustrates a roadwith three lanes and four vehicles V1, V2, V3 and V4. At least thevehicles V1 and V4 are equipped with radar sensors. The radar sensor ofthe vehicle V1 emits an RF radar signal s_(RF)(t) and the received RFradar signal y_(RF)(t) contains the radar echoes from the vehicles V2and V3 in front and the oncoming vehicle V4. The RF radar signaly_(RF)(t) received by the radar sensor of the vehicle V1 furthermorecontains a radar signal (interfering signal) that was generated by theradar sensor of the oncoming vehicle V4. The radar sensor of the vehicleV4 is an interferer for the radar sensor of the vehicle V1.

The signal y_(RF)(t) received by the radar sensor of the vehicle V1 maybe written as follows in the case of U radar targets and V interferers:y _(RF)(t)=y _(RF,T)(t)+y _(RF,I)(t), wherein  (1)y _(RF,T)(t)=Σ_(i=0) ^(U−1) A _(T,i) ·s _(RF)(t−Δ _(T,i)) and  (2)y _(RF,I)(t)=Σ_(k+0) ^(V−1) A _(l,k) ·s _(RF,k)′(t−Δt _(l,k)).  (3)In the above equations (1) to (3), the signal components y_(RF,T)(t) andy_(RF,I)(t) of the received signal y_(RF)(t) correspond to the radarechoes from real radar targets T_(i) and the interfering signals. Aplurality of radar echoes and a plurality of interferers may be presentin practice. Equation (2) therefore represents the sum of the radarechoes that are caused by U different radar targets T_(i), whereinA_(T,i) denotes the attenuation of the emitted radar signal and Δt_(T,i)denotes the outward and return propagation time (round trip delay time,RTDT) for a particular radar target T_(i). Similarly, equation (3)represents the sum of the interfering signals that are caused by Vinterferers. In this case, A_(l,k) denotes the attenuation of theinterfering signal s_(RF,k)′(t) emitted by an interferer and Δt_(l,k)represents the associated signal propagation time (for each interfererk=0, 1, . . . , V−1). It is noted that the radar signal s_(RF)(t)emitted by the vehicle V1 and the interfering signal s_(RF,0)′(t)emitted by the vehicle V4 (index k=0 for vehicle V4) will generally havedifferent chirp sequences with different chirp parameters (start/stopfrequency, chirp duration, repetition rate, etc.). The amplitude of thereceived interfering signal component y_(RF,I) (t) may furthermore beconsiderably higher than the amplitude of the echo signal componenty_(RF,T)(t).

FIG. 5 illustrates one example implementation of a radar transceiver 1according to the example from FIG. 3 in more detail. The present examplein particular illustrates the RF front end 10 of the radar transceiver 1and the RF front end 10′ of another (interfering) radar sensor 1′. It isnoted that FIG. 5 illustrates a simplified circuit diagram in order toshow the fundamental structure of the RF front end 10 with onetransmission channel (TX channel) and one reception channel (RXchannel). As mentioned, actual implementations, which may depend greatlyon the specific application, are usually more complex and have aplurality of TX and/or RX channels.

The RF front end 10 comprises a local oscillator 101 (LO) that generatesan RF oscillator signal s_(LO)(t). During operation—as described abovewith reference to FIG. 2 —the RF oscillator signal s_(LO)(t) isfrequency-modulated and is also referred to as LO signal. In radarapplications, the LO signal is usually in the SHF (super high frequency,centimeter wave) or in the EHF (extremely high frequency, millimeterwave) band, for example in the interval from 76 GHz to 81 GHz in someautomotive applications. The LO signal s_(LO)(t) is processed both inthe transmission signal path TX1 (in the TX channel) and in thereception signal path RX1 (in the RX channel).

The transmission signal s_(RF)(t) (cf. FIG. 2 ) emitted by the TXantenna 5 is generated by amplifying the LO signal s_(LO)(t), forexample by way of the RF power amplifier 102, and is thus merely anamplified and possibly phase-shifted version of the LO signal s_(LO)(t).The output of the amplifier 102 may be coupled to the TX antenna 5 (inthe case of a bistatic or pseudo-monostatic radar configuration). Thereception signal y_(RF)(t) received by the RX antenna 6 is fed to thereceiver circuit in the RX channel and therefore directly or indirectlyto the RF port of the mixer 104. In the present example, the RFreception signal y_(RF)(t) (antenna signal) is pre-amplified by way ofthe amplifier 103 (amplification g). The mixer 104 thus receives theamplified RF reception signal g y_(RF)(t). The amplifier 103 may be forexample an LNA (low-noise amplifier). The LO signal s_(LO)(t) is fed tothe reference port of the mixer 104, such that the mixer 104 downmixesthe (pre-amplified) RF reception signal y_(RF)(t) into baseband. Thedownmixed baseband signal (mixer output signal) is referred to asy_(BB)(t). This baseband signal y_(BB)(t) is initially processed furtherin an analog manner, wherein the analog baseband signal processing chain20 essentially brings about amplification and (for example band-pass orlow-pass) filtering in order to suppress undesired sidebands and mirrorfrequencies. The resulting analog output signal, which is fed to ananalog-to-digital converter (see FIG. 3 , ADC 30), is referred to asy(t). Methods for the digital further processing of the digitized outputsignal (digital radar signal y[n]) are known per se (for example rangeDoppler analysis) and are therefore not discussed in more detail here. Afew basics of range Doppler analysis are however explained further belowwith reference to FIG. 9 .

In the present example, the mixer 104 downmixes the pre-amplified RFreception signal g·y_(RF)(t) (that is to say the amplified antennasignal) into baseband. The mixing may be performed in one stage (that isto say from the RF band directly into baseband) or over one or moreintermediate stages (that is to say from the RF band into anintermediate frequency band and further into baseband). In this case,the reception mixer 104 effectively comprises a plurality of individualmixer stages connected in series. With regard to the example shown inFIG. 5 , it becomes clear that the quality of a radar measurementdepends strongly on the quality of the LO signal s_(LO)(t), for exampleon the noise contained in the LO signal s_(LO)(t), which is determinedin terms of quantity by the phase noise of the local oscillator 101. Asimple mixer is used in the illustrated example. As an alternative, inother example implementations, IQ mixers may also be used in order togenerate complex baseband signals (in-phase and quadrature components).

FIG. 5 furthermore shows part (the TX channel of the RF front end 10′)of a further radar sensor 1′ that constitutes an interferer for theradar sensor 1. The RF front end 10′ of the radar sensor 1′ contains afurther local oscillator 101′ that generates an LO signal s_(LO)′(t)that is amplified by the amplifier 102′. The amplified LO signal isemitted, as RF radar signal s_(RF,0)′(t), by the antenna 5′ of the radarsensor 1′ (cf. equation (3)). This RF radar signal s_(RF,0)′(t)contributes to the interfering signal component y_(RF,I)(t) received bythe antenna 6 of the other radar sensor 1 and may cause theinterference.

FIG. 6 schematically illustrates an example of an FM scheme as isusually used in FMCW radar sensors in the frequency modulation (FM) ofthe LO signal s_(LO)(t). A sequence of chirps is generated for eachmeasurement in the illustrated example. The first sequence contains only16 chirps in FIG. 6 . In practice, however, a sequence may containconsiderably more chirps, for example 128 or 256 chirps. A number thatcorresponds to a power of two makes it possible to use efficient FFT(fast Fourier transform) algorithms in the subsequent digital signalprocessing (for example in range Doppler analysis). There may be a pausebetween the individual sequences.

FIGS. 7 and 8 illustrate, on the basis of an example, how an interfereris able to interfere with the radar echoes that are contained in the RFsignal y_(RF)(t) received by the radar sensor 1. FIG. 7 shows a graph(frequency over time) of a chirp, emitted by the radar sensor 1, with achirp duration of 60 μs (solid line). The start frequency of the emittedsignal s_(RF)(t) is approximately 76250 MHz, and the stop frequency isapproximately 76600 MHz. An interfering signal y_(RF,I)(t) generated byanother radar sensor contains an up-chirp with a start frequency ofapproximately 76100 MHz, a stop frequency of approximately 76580 MHz anda chirp duration of 30 μs and a subsequent down-chirp that starts at thestop frequency of the preceding chirp and ends at the start frequency ofthe preceding chirp and has a chirp duration of 10 μs (dot-and-dashline). The bandwidth B of the baseband signal of the radar sensor isdefined essentially by the baseband signal processing chain 20 and isindicated by the dashed lines in FIG. 7 . FIG. 8 shows an example signalprofile of the (pre-processed) baseband signal y(t) of the radar sensor1. It is able to be seen that the signal components caused by theinterference in that time interval at which the frequency of theinterfering signal lies within the bandwidth B of the radar sensor havea significant amplitude (see FIGS. 7 and 8 ). In the present example,the interference occurs three times during the chirp duration of 60 μs,specifically at approximately 7 μs, 28 μs and 42 μs. As mentioned, thepower of the interfering signal may be higher than the power of theradar echo from real targets. The interfering signals and thetransmission signal of the radar sensor 1 under consideration arefurthermore uncorrelated (other than exceptions that are not consideredhere), for which reason the interference may be considered to be noise(within the meaning of broadband interference) and thus increases thenoise floor.

Before interfering signal suppression is discussed in more detail, abrief summary is given below of the signal processing usually performedin a radar sensor in order to detect radar targets. FIG. 9 illustrates,with reference to an example, the analog signal processing of a radarsensor as far as the digitization of the baseband signal that representsthe chirp echo signals, and the subsequent digital processing. Graph (a)in FIG. 9 shows part of a chirp sequence that comprises M linear chirps.The solid line represents the signal profile (waveform, frequency overtime) of the outgoing RF radar signal s_(RF)(t), and the dashed linerepresents the corresponding signal profile of the arriving (andpre-amplified) RF radar signal y_(RF)(t) that (when present) containsthe chirp echoes. According to graph (a) in FIG. 9 , the frequency ofthe outgoing radar signal increases linearly, starting at a startfrequency f_(START), up to a stop frequency f_(STOP)(chirp no. 0) andthen drops back to the start frequency f_(START), increases again up tothe stop frequency f_(STOP) (chirp no. 1), and so on.

As explained above with reference to FIG. 6 , a chirp sequence comprisesa plurality of chirps; in the present case, the number of chirps in asequence is denoted M. Depending on the application, a sequence may alsocontain chirps with different parameters (start and stop frequency,duration and modulation pause). During a modulation pause between twosuccessive chirps, the frequency may for example be the same as the stopfrequency of the previous chirp or the start frequency of the followingchirp (or the same as another frequency). The chirp duration may be inthe range from a few microseconds up to a few milliseconds, for examplein the range from 20 μs to 2 ms. The actual values may also be greateror smaller depending on the application. The number M of chirps in asequence may correspond to a power of two, for example M=256.

The arriving RF radar signal y_(RF)(t) (that is to say received by theRX antenna) lags the outgoing RF radar signal s_(RF)(t) (that is to sayemitted by the TX antenna) by a time difference Δt. This time differenceΔt corresponds to the signal propagation time from the TX antenna to theradar target and back to the RX antenna, and is also referred to asround trip delay time (RTDT). The distance d_(T) _(i) of a radar targetT_(i) from the radar sensor is d_(T) _(i) =c·Δt/2, that is to say thespeed of light c times half the time difference Δt. As is able to beseen in graph (a) in FIG. 9 , the time difference Δt results in acorresponding frequency difference Δf. This frequency difference Δf maybe determined by mixing the arriving (and possibly pre-amplified) radarsignal y_(RF)(t) with the LO signal s_(LO)(t) of the radar sensor (seeFIG. 5 , mixer 104), digitizing the resulting baseband signal y(t) andthen performing digital spectral analysis. The frequency difference Δfthen appears in the spectrum of the digitized baseband signal y[n] aswhat is called the beat frequency. If linear chirps are used, the timedifference Δt may be calculated according to Δt=Δf/k, wherein the factork denotes the gradient (hertz per second) of the frequency ramp that isable to be calculated according to k=B/T_(CHIRP), wherein B is thebandwidth of a chirp (B=|f_(STOP)−f_(START)|). With regard to the aboveexplanations, it follows, for the sought distance d_(T) _(i) of thetarget T_(i):d _(T) _(i) =c·Δt/2=c·Δf·T _(CHIRP)/(2B)  (4)

Although the basic functional principle of an FMCW radar sensor has beensummarized above, it is noted that more sophisticated signal processingis usually applied in practice. By way of example, an additional Dopplershift f_(D) of the arriving signal caused by the Doppler effect mayinfluence the distance measurement, this adding the Doppler shift f_(D)to the frequency difference Δf explained above. Depending on theapplication, the Doppler shift may be estimated/calculated from theoutgoing and arriving radar signals and be taken into consideration inthe measurement, whereas the Doppler shift may be negligible for thedistance measurement in some applications. This may for example be thecase when the chirp duration is high and the speed of the target is low,such that the frequency difference Δf is large in comparison with theDoppler shift f_(D). In some radar systems, the Doppler shift may beeliminated by determining the distance based on an up-chirp and adown-chirp in the distance measurement. In theory, the actual distanced_(T) may be calculated as the average of the distance values obtainedfrom a measurement using up-chirps and a further measurement usingdown-chirps. The Doppler shift is eliminated through the averaging.

One example of a signal processing technique for processing FMCW signalsinvolves calculating what are known as range Doppler maps, which arealso referred to as range Doppler images. In general, FMCW radar sensorsdetermine the target information (that is to say distance, speed, DoA)by emitting a sequence of chirps (see FIG. 9 , graph (a)) and mixing the(delayed) echoes from the radar targets with a “copy” of the emittedsignal (cf. FIG. 5 , mixer 104). The resulting baseband signal y(t) isillustrated in graph (b) in FIG. 9 . This baseband signal y(t), andtherefore also the digitized baseband signal y[n] (digital radarsignal), may be divided into a plurality of segments, wherein eachsegment of the digital radar signal y[n] is associated with a particularchirp of the chirp sequence.

The target information may be extracted from the spectrum of thesegments of the digital radar signal y[n], containing the chirp echoesgenerated by one or more radar targets. A range Doppler map is forexample obtained, as explained in more detail below, by way of atwo-stage Fourier transformation. Range Doppler maps may be used as abasis for various methods for detecting, identifying and classifyingradar targets. The result of the first Fourier transformation stage isreferred to as a range map. The methods described here for interferingsignal suppression may be performed in the segments of the digital radarsignal and/or their spectra that are contained in such a range map.

In the examples illustrated here, the calculations to determine therange Doppler maps are performed by a digital computing unit, such asfor example a signal processor (cf. FIG. 5 , a computing unit 40, suchas DSP 40). In other example implementations, in addition or as analternative to a signal processor, other computing units may also beused in order to perform the calculations. Depending on theimplementation, the calculations may be performed by different softwareand hardware entities or combinations thereof. The term computing unitmay typically be understood to mean any combination of software andhardware that is capable of and designed to perform the calculationsthat are described in connection with the example implementationsexplained here.

According to one example implementation, the calculation of a rangeDoppler map involves two stages, wherein a plurality of Fouriertransformations are calculated in each stage (for example by way of anFFT algorithm). According to the present example, the baseband signaly(t) (cf. FIG. 5 ) is sampled such that N×M sampled values (samples),that is to say M segments each containing N samples, are obtained for achirp sequence containing M chirps. That is to say, the sampling timeinterval T_(SAMPLE) is selected such that each of the M segments (chirpechoes in baseband) is represented by a sequence of N samples. Asillustrated in diagram (c) in FIG. 9 , these M segments with in eachcase N samples may be arranged in a two-dimensional array Y[n, m] (radardata array). Each column of the array Y[n, m] represents one of the Msegments under consideration of the baseband signal y(t), and the nthrow of the array Y[n, m] contains the nth sample of the M chirps. Therow index n (n=0, 1, . . . N−1) may thus be considered to be a discretetime n T_(SAMPLE) (within a chirp) on a “fast” time axis. Similarly, thecolumn index m (m=0, 1, . . . M−1) may be considered to be a discretetime m·T_(CHIRP) on a “slow” time axis. The column index m correspondsto the number of the chirp in a chirp sequence.

In a first stage, a first FFT (usually referred to as range FFT) isapplied to each chirp. The Fourier transformation is calculated for eachcolumn of the array Y[n, m]. In other words, the array Y[n, m] isFourier-transformed along the fast time axis, and a two-dimensionalarray Y[n, m] of spectra, referred to as range map, is obtained as aresult, wherein each of the M columns of the range map in each casecontains N (complex-value) spectral values. By virtue of the Fouriertransformation, the “fast” time axis becomes the frequency axis; the rowindex k of the range map R[k, m] corresponds to a discrete frequency andis therefore also referred to as frequency bin. Each discrete frequencycorresponds to a distance according to equation 4, for which reason thefrequency axis is also referred to as distance axis (or range axis).

The range map R[k, m] is illustrated in diagram (c) in FIG. 9 . A radarecho caused by a radar target results in a local maximum (peak) at aparticular frequency index/frequency bin. This local maximum usuallyappears in all of the columns of the range map R[k, m], that is to sayin the spectra of all of the segments under consideration of thebaseband signal y[n] that are able to be associated with the chirps of achirp sequence. As mentioned, the associated frequency index k (forexample in accordance with equation 4) may be converted into a distancevalue.

In a second stage, a second FFT (usually referred to as Doppler FFT) isapplied to each of the N rows of the range map R[k, m] (k=0, . . . ,N−1). Each row of the range map R[k, m] contains M spectral values of aparticular frequency bin, wherein each frequency bin corresponds to aparticular distance d_(T) _(i) of a particular radar target T_(i). TheFourier transformation of the spectral values in a particular frequencybin (able to be associated with a radar target) makes it possible todetermine the associated Doppler shift f_(D) that corresponds to a speedof the radar target. In other words, the two-dimensional array R[k, m](the range map) is Fourier-transformed in rows, that is to say along the“slow” time axis. The resulting Fourier transforms again form an arraycontaining N×M spectral values, which is referred to as range Dopplermap X[k, l] (k=0, . . . , N−1 und 1=0, . . . , M−1). The “slow” timeaxis becomes the Doppler frequency axis through the second FFT. Theassociated discrete Doppler frequency values each correspond to aparticular speed. The Doppler frequency axis may accordingly beconverted into a speed axis. In the examples described here, thedimensions of the matrices Y[n, m], R[k, m] and X[k, l] are equal toN×M, wherein N denotes the number of discrete frequency values (alsoreferred to as frequency bins or bins) on the range axis and M denotesthe number of discrete Doppler frequency values on the Doppler axis.This is however not necessarily the case. Depending on theimplementation, the matrix Y[n, m] may be expanded (virtually) by way ofzero padding, such that the range map R[k, m] has a dimension N′×M,where N′>N. It is mentioned for the sake of completeness that it mayalso be the case that N>N′, for example if particular distance rangesare not required. The same applies analogously to the parameter M(number of signal segments/chirps in a sequence) with regard to thespeed.

Each local maximum (each peak) in the range Doppler map X[k, l]indicates a potential radar target. The row index k (on the range axis)associated with a local maximum represents the distance of the target,and the column index l (on the speed axis) associated with the localmaximum represents the speed of the target.

Several variants of a concept for detecting and/or reducing (for exampleinterference-induced) disturbances contained in the measured valuescontained in a radar data array Y[n, m] are described below. The mthcolumn of a radar data array Y[n, m]—that is to say the mth segment of asequence of M segments of digital radar signal—is denoted y_(m)[n]below. Each signal segment y_(m)[n] may be associated with a particularchirp of a particular chirp sequence of the emitted RF radar signals_(RF)(t). FIGS. 10A-10D illustrates various example structures ofsuppressing/filtering or detecting interference-induced (and other)disturbances using artificial neural networks (ANNs).

The example shown in FIG. 10A relates to the detection of signalsegments y_(m)[n] that are impacted by interference using one or moreartificial neural networks, and the subsequent digital signal processingin order to detect radar targets. The functional blocks illustrated inFIGS. 10A-10D may be implemented at least partly in software that isexecuted by one or more processors. These processors may for example becontained in the computing unit 40 and/or the controller 50 (cf. FIG. 3). The functions provided by the functional blocks illustrated in FIGS.10A-10D may also however be implemented at least partly by way ofhard-wired ALUs (arithmetic logic units). By way of example, fastFourier transform (FFT) algorithms are able to be implementedcomparatively easily by way of hardware. In particular neural networksmay be implemented by way of circuits (for example neuromorphiccircuits) and processors (for example neuromorphic/neurosynapticprocessors) that are specifically suitable for this purpose. Processorswith hardware accelerators for artificial neural networks (within themeaning of fast vector signal processors having a high number ofparallel multiplier-accumulate (MAC) units) likewise exist.

According to FIG. 10A, the M signal segments y_(m)[n] (m=0, . . . , M−1,n=0, . . . , N−1) are Fourier-transformed twice (range FFT 41, DopplerFFT block 42) in order to obtain a range Doppler map X[k, l]. The signalsegments y_(m)[n] are furthermore fed to the artificial neural network44. The input of the artificial neural network 44 thus obtains, as inputvector, the N sampled values y_(m)[0] to y_(m)[N−1] for each receivedsignal segment m (that is associated with the respective chirp). Theartificial neural network is trained to detect whether the respectivesignal segment or some of the samples contained therein are impacted byinterference. The result of this detection is a yes/no decision (binaryresult) either for the respective signal segment on its own or for eachindividual sample contained therein. FIGS. 11 and 13 show examples ofsuitable neural network structures in more detail. The target detection(functional block 43) may then be performed based on the calculatedrange Doppler map X[k, l] and taking into consideration the outputsignal/the output signals from the neural network 44. In one modifiedexample (not illustrated), the column vectors r_(m)[k] of the range mapR[l, m] are fed to the neural network 44 as input data instead of thesignal segments y_(m)[n].

The example shown in FIG. 10B relates to the reduction of interferingsignal components in the signal segments y_(m)[n] using one or moreartificial neural networks, and the subsequent digital signal processingin order to detect radar targets. In this connection, the neural network44′ may be considered to be a filter that is designed to reduce orideally to eliminate the interfering signal components that may becontained in the signal components y_(m)[n]. The desired filtercharacteristics may then be achieved by appropriately training theneural network 44′. The filtered signal segments (output signal from theneural network) are denoted ŷ_(m)[n] in FIG. 10B. The subsequentcalculation of the range FFT (block 41) and the Doppler FFT (block 42)and the target detection (block 43) is based on the filtered signalsegments ŷ_(m)[n] that may be combined to form a filtered radar dataarray Ŷ[n, m] in which the interfering signal components have beenreduced (ideally eliminated). The range FFT and Doppler FFT may becalculated in accordance with FIG. 8 .

The example shown in FIG. 10C is highly similar to the previous examplefrom FIG. 10B, apart from the fact that the artificial neural network44″ is arranged between the range FFT block 41 and the Doppler FFT block42. The neural network 44″ thus receives, as input data, not the signalsegments y_(m)[n] in the time domain, but rather in the frequencydomain, that is to say the discrete spectra r_(m)[k] that represent thecolumns of the range map R[k, m], that is to say r_(m)[k] is the mthcolumn of the range map R[k, m]. The neural network 44″ delivers, asoutput data, the filtered spectra {circumflex over (r)}_(m)[k], whichmay be combined to form a filtered range map {circumflex over (R)}[k,m]. In this example, the range Doppler map X[k, l] is obtained byFourier-transforming the filtered range map {circumflex over (R)}[k, m](range FFT block 42). The target detection (functional block 43) may beperformed as in the previous example.

In a further, modified example, the artificial neural network isarranged between Doppler FFT (functional block 42) and target detection(functional block 43), such that the filtering brought about by theneural network “filtering” is performed in the Doppler frequency domain.This variant is illustrated in FIG. 10D. In this implementation, thefiltering is two-dimensional. By way of example, filtering with atwo-dimensional filter mask (kernel) may be used in a convolutionalneural network. Example implementations of this variant are described inmore detail further below in connection with FIGS. 14 to 16 .

FIG. 11 illustrates an example implementation of a neural network 44 asmay be used for example in the example from FIG. 10A. In the presentexample, the digital radar signal of each RX channel is processed insegments. That is to say, a signal segment y_(m)[n](that is to say themth column of the radar data array Y[n, m]) may be considered to be avector of input data of the neural network 44, wherein the vector has Nvalues and may be associated with the sampled values of a particularchirp of the emitted RF chirp sequence. The input vector y_(m)[n] at thesame time forms the input layer L₀ of the neural network 44. The neuralnetwork generally has a plurality of layers L₁ to L_(S), wherein thelast layer L_(S) is referred to as output layer that delivers the outputdata. In the present example, these output data are N binary (Boolean)values of an output vector that indicate whether the correspondingvalues of the input vector are impacted by noise or interference-induceddisturbances.

According to the structure, shown in FIG. 11 , of a neural network, eachof the layers L₁ to L_(S) has N neurons, wherein the output valueL_(S)[n] of the nth neuron of the sth layer may be determined asfollows:L _(S) [n]=φ(Σ_(N−1) ^(i=0) w _(s,n) [i]·L _(S−1) [i]), for s=1, . . .S.  (5)The function φ(•) is usually called activation function, which istypically nonlinear. In other words, each neuron of a layer of theneural network determines a weighted sum of the output values from theprevious layer and applies the activation function φ(•) to this weightedsum. In above equation (5), the weighting factors of the nth neuron inthe sth layer L_(S) are denoted w_(s,n)[i], wherein the index i (i=0, .. . , N−1) denotes the associated input value L_(S−1) [i] that isdelivered as output value by the previous layer. As mentioned, the layerL₀ denotes the input data vector y_(m)[n] (where n=0, . . . , N−1).

The weighting factors w_(s,n)[i] are determined by training the neuralnetwork. In one example implementation, the neural network is what isknown as a convolutional neural network in which the output valueL_(S)[n] of a neuron does not depend on all of the output valuesL_(S−1)[i], but only on the “adjacent” values. In this case, equation 5may be modified as follows.L _(S) [n]=φ(Σ_(i=n−1) ^(n+1) w _(s,n) [i]·L _(S−1) [i]), for s=1, . . .S,  (6)wherein the weighting factors w_(s,n)[−1] and w_(s,n)[N] may be zero(that is to say the weighting factors are supplemented with zeros at the“edge”), and wherein the index n=0, . . . N−1 denotes a particularneuron in the respective layer. In this case, the weighted sum inequation 6 may also be considered to be a convolution of the outputvector L_(S−1)[i] of the previous layer with the kernel vectorw_(s,n)=(w_(s,n)[−1], w_(s,n)[0], w_(s,n)[1]), and equation 6 may bewritten as follows:L _(S) [n]=φ(Σ_(i=−1) ¹ w _(s) [i]·L _(S−1) [n−i])=φ((w _(s,n) *L_(S−1))[n]),  (7)wherein the operator * denotes the discrete convolution. Equations 6 and7 relate to a special case with a kernel vector w_(s)[i] having threeelements. It is understood that kernel vectors having more than threeelements may also be used. The kernel vector may also be referred to asconvolution core, filter core, filter mask or simply just as kernel. Inthe case of a two-dimensional convolution (cf. FIG. 14 ), the kernel isa (two-dimensional) kernel matrix.

The activation function φ(•) is typically a nonlinear function.Different activation functions may be used depending on the application.Examples of customary activation functions are the step function, thesigmoid function or what is known as an ReLU function, wherein theabbreviation ReLU stands for rectifier linear unit. The ReLU function isusually defined as follows: ReLU(x)=max{0, x}. The step function has thevalue 1 if its argument (that is to say the weighted sum from equations5 to 7) is greater than or equal to a threshold value θ_(s,n), andotherwise the value 0.

The example from FIG. 12 is highly similar in terms of structure to theexample from FIG. 11 , and reference is made to the above description.Unlike in the example from FIG. 11 , the neural network 44′ however doesnot deliver any binary (Boolean) output values (yes/no decision), butrather a filtered version ŷ[n] of the signal segment y_(m)[n]. Afiltered radar data array Y[n, m] that may serve as a basis for a rangeDoppler analysis may be constructed from the filtered signal segments.The neural network 44′ will in this case typically not use a stepfunction as activation function φ(•), but rather for example an ReLUfunction (since the output data are not Boolean values). For the rest,the neural network 44′ differs from the neural network 44 from theprevious example primarily through the training data that are used, byway of which the neural network is trained in a manner known per se.

The example from FIG. 13 may be considered to be a special case of theexample from FIG. 11 . The neural network 44″ is highly similar in termsof structure to the neural network 44 from the example from FIG. 11 ,wherein—unlike in FIG. 11 —only one neuron that delivers a Booleanoutput value is used in the output layer L_(S) of the neural network44″. A plurality of neurons (up to N neurons) are used in the otherlayers L₁ to L_(S−1). Since the neuron of the output layer L_(S)delivers a Boolean value, a step function is used as activation functionφ(•) in the present example—at least in the last layer. In general, thesame activation function does not have to be used in each layer of aneural network.

The examples from FIGS. 11 and 13 correspond to the signal processingstructure from FIG. 10A. The example from FIG. 12 corresponds to thesignal processing structure from FIG. 10B. As already mentioned, thecorresponding values r_(m)[k] of the range map R[k, m] may also be usedas input data (r_(m)[k]=R[k, m]) instead of the signal segmentsy_(m)[n]. In this case, the signal processing structure corresponds tothat from FIG. 10C. Since the range map R[k, m], and thus also thespectra r_(m)[k], have complex values, the real part and imaginary partmay be filtered using separate neural networks.

According to a further example, the filtering (denoising) is performedon the basis of the range Doppler map using a convolutional neuralnetwork (CNN), wherein a two-dimensional kernel (having for example 3×3elements) is used. In this case, the kernel is also referred to as aconvolutional matrix or mask. FIG. 14 illustrates the filtering(denoising) of a range Doppler map X[k, l], which may be considered tobe a N×M-matrix (that is to say k=0, . . . , N−1 and l=0, . . . , M−1).The real part Re{X[k, l]} and imaginary part lm{X[k, l]} each form theinput layers L₀ of a separate neural network, that is to say the realpart and imaginary part are processed separately. FIG. 14 in this caseessentially shows only the general principle of filtering by way of aconvolutional neural network. An example implementation is explained inmore detail further on with reference to FIG. 15 .

The layers L₁ to L_(S) of the two neural networks each containN×M-neurons that contain the output values L_(S)[k, l] (for k=0, . . . ,N−1, l=0, . . . , M−1 and s=1, . . . , S), wherein L₀[k, l]=X[k, l]. Theoutput values L_(S)[k, l] may be calculated in the same way as equation7 as follows:L _(S) [k,l]=φ(Σ_(k=−1) ¹Σ_(i=−1) ¹ w _(s) [i,j]·L _(S−1)[k−i,l−j]),  (8)wherein the ReLU function may for example be used as activation functionφ(•). The weights w_(s)[i,j] contained in the kernel are determined bytraining the neural networks. In order to be able to completelycalculate the convolution according to equation 8, the N×M-matricesL_(S−1)[k, l] are expanded for example by way of zero padding, such thatL_(S−1)[k, l] are also defined for k<0 and k≥N as well as l<0 and k≥M.

In one example implementation, the values of the input layer L₀[k, l]may be normalized, such that for example the average is zero and thestandard deviation is one. Such normalization of the values contained inthe N×M-matrix L₀[k, l] may be achieved through offset compensation(such that the average is zero) and scaling (such that the standarddeviation is one). That is to say, in this case the values of the inputlayer L₀[k, l] are equal to a·(Re{X[k, l]}−Re{X}), wherein a is thescaling factor and Re{X} is the average of the real parts of the rangeDoppler map. The imaginary part Im{X[k, l]} may be normalized in thesame way. Other types of normalization may likewise be used (for examplein the interval from 0 to 1).

In the various layers of L₁ to L_(S−1) of the neural networks, what isknown as batch normalization may however be performed on the result ofthe convolution operation before applying the activation function φ(•).In this case, it is likewise attempted to bring the average of theoutput values of a layer (which are then the input values of the nextlayer) to zero and their standard deviation to 1. Performing batchnormalization improves the stability of neural networks and may forexample be taken from the publication S. Ioffe, C. Szegedy, BatchNormalization: Accelerating Deep Network Training by Reducing InternalCovariale Shift”, arXiv: 1502.03167v3 [cs.LG], Mar. 2, 2015.

Depending on the actual implementation, the number of layers used in theneural networks may be different. Acceptable results may be achieved inmany cases with four to ten layers. In one experiment, the performanceof the denoising dropped with fewer than six layers and increasedconsiderably with more than six layers. Good results were able to beachieved using kernels of dimension 3×3 and 5×5, provided that enough(for example more than six) layers are provided in the neural network.In the case of larger kernels (for example 7×7 elements), there is therisk of the form and the extent of the detected local maxima (peaks) inthe filtered range Doppler map being blurred, which may impair thequality of the detection of radar targets. Larger kernels may however beused depending on the specification of the radar system.

In the example according to FIG. 14 , a range Doppler map is filtered inits entirety. As an alternative, the denoising may also be performedbased on the range map. In this example, the M spectra contained in arange map R[k, m] are processed separately (and the real and imaginarypart again separately). In this case, the input data are M×1-vectors andthe kernel may have for example 25×1 elements. The convolution operationis in this case a one-dimensional operation, as already discussed abovewith reference to FIG. 12 (see also equation 7).

In the example from FIG. 14 , the range Doppler map X[k, l] forms theinput layer L₀ of the neural network. The range Doppler map X[k, l]typically contains N×M complex numerical values, which are the result ofa two-dimensional FFT (cf. FIG. 9 ). The parameter M in this casedenotes the number of frequency bins on the Doppler axis, and theparameter N denotes the number of frequency bins on the range axis (cf.explanations regarding FIG. 9 ). In the following example, the real partRe{X[k, l]} and imaginary part Im{X[k, l]} of the range Doppler map X[k,l] under consideration are considered to be separate “NN channels” (NNstands for neural network). That is to say, the input layer L₀ of theneural network has two NN channels, which are sometimes also referred toas “maps” (not to be confused with the range Doppler map). The outputlayer L_(S) of the neural network likewise has two NN channels thatrepresent the real part Re{{circumflex over (X)}[k, l]} and imaginarypart Im{X[k, l]} of the filtered range Doppler map {circumflex over(X)}[k, l], in which noise and (for example interference-induced)disturbances are reduced (ideally eliminated). The other layers L₁ toL_(S−1) may likewise have two or more NN channels. In the example fromFIG. 15 , in each case sixteen NN channels are used in the layers L₁ toL_(S−1). The number of NN channels in the layers L₁ to L_(S−1) ishowever not necessarily the same. The layers L₁ to L_(S) are alsoreferred to as convolutional layers. The NN channels of the individuallayers of a neural network are not to be confused with the TX and RXchannels of the RF front end of the radar system.

In the example from FIG. 15 , the layer L₁ processes the output valuesof the two NN channels of the layer L₀ as input data. The output valuesof the sixteen NN channels of the layers L₁ to L_(S−1) form the inputvalues of the respective following layer L₂ to L_(S). A kernelassociated with the respective NN channel and that contains weightingfactors as explained above is used in each layer L₁ to L_(S) tocalculate the output values of each NN channel. In the example from FIG.15 , in each case sixteen kernels are therefore used in the layers L₁ toL_(S−1) (one kernel for each NN channel), whereas only two kernels areused in the layer L_(S) (likewise one for each NN channel). The kernelsare however not two-dimensional convolutional matrices, but ratherthree-dimensional arrays each containing K₁×K₂×C_(in) elements. In thiscase, K₁×K₂ denotes the dimension of a kernel within an NN channel andC_(in) denotes the number of NN channels that deliver input data. In theexample illustrated in FIG. 15 , the sixteen kernels of the layer L₁have a dimension of 3×3×2, the sixteen kernels of the layers L₁ toL_(S−1) have a dimension of 3×3×16, and the two kernels of the outputlayer L_(S) likewise have a dimension of 3×3×16. It is understood thatthe numerical values that are given are merely examples. The values K₁and K₂ are also not necessarily the same.

In the case of a plurality of NN channels, the convolution may takeplace in the same way as equation 8, wherein summing is additionallyperformed over all of the NN channels of the previous layer.L _(S) [k,l,c]=φ(Σ_(u=1) ^(Cin)Σ_(j=−1) ¹Σ_(i=−1) ¹ w _(s) [i,j,u]·L_(S−1) [k−i,l−j,c−u]).  (9)In above equation 9, L_(S)[k, l, c] denotes the output values of the cthNN channel of the sth layer. Equation 9 is a generalization of equation8, wherein the sum of u=1 to C_(in) denotes the summing over all of theC_(in) NN channels of the respective previous layer. For the layer L₁,C_(in) would be equal to two (the input layer L₀ has two NN channels),for the layers L₁ to L_(S), C_(in) would be equal to sixteen in theexample from FIG. 15 . As mentioned, equation 9 represents the specialcase in which the kernels w_(s)[k, l, c] have 3×3×C_(in) elements. Inother examples, more than three elements may also be used in the firsttwo dimensions (range and Doppler dimension).

Unlike in known applications (for example in image processing in orderto classify objects contained in images), the neural network in theexamples described here does not necessarily end with a fully connectedlayer, but rather with a “normal” convolutional layer. The output layerL_(S), unlike the layers L₁ to L_(S−1), may use a linear activationfunction φ(•), for example φ(a)=a (for any argument a). Due to the factthat precisely two kernels are used in the output layer L_(S), twooutput channels each having N×M values that represent the real part andimaginary part of a filtered range Doppler map are obtained.

Unlike in known applications, such as for example in image processing inorder to classify objects contained in images, pooling is notnecessarily performed in the example implementations described here.Pooling generally leads to a lossy reduction in the amount of data,which may generally be undesired in radar applications. The number ofoutput values of each NN channel of each layer is generally N×M in theexample implementations described here and therefore corresponds to thenumber of (complex) values of the range Doppler map.

FIG. 16 illustrates one example of a signal processing structure withinwhich denoising of the range Doppler maps is performed. In theillustrated example, consideration is given to a radar system with i RXchannels RX1, RX2, . . . , RXi. The RX channels may also be virtualchannels, that is to say channels in which each channel is in each caseassociated with a particular combination of transmission and receptionantenna, for example in an MIMO arrangement. Each channel delivers adigital radar signal y[n] that is processed in segments, wherein eachsignal segment l y_(m)[n] may be associated with a particular chirp ofan emitted RF chirp sequence. In a first step, a range FFT is calculatedfor each channel (functional block 41), such that a range map R[k, m] isobtained for each channel (see also FIG. 9 ). In a second step, aDoppler FFT is calculated for each channel (functional block 42), suchthat a range Doppler map X[k, l] is obtained for each channel. The rangeDoppler maps are fed (sequentially or at least partly in parallel) toneural networks (functional block 44), wherein the real and imaginarypart are each processed separately. The neural networks may beconvolutional neural networks, as described above with reference to FIG.14 . The functional block 44 delivers a filtered range Doppler map{circumflex over (X)}[k, l] for each channel as output data. In orderalso to be able to determine the direction of arrival (DoA) of the RFradar signals, angle FFTs are also calculated based on the unfilteredrange Doppler map X[k, l] in a manner known per se. The target detection(functional block 43) is then performed based on the filtered rangeDoppler maps {circumflex over (X)}[k, l]. As an alternative, the angleFFTs may also be calculated on the basis of the filtered range Dopplermaps {circumflex over (X)}[k, l].

Calculating the angle FFTs on the basis of the unfiltered range Dopplermaps X[k, l] may however offer the advantage that, although the positionand speed may still be determined on the basis of the filtered rangeDoppler maps {circumflex over (X)}[k, I] (which significantly increasesreliability and accuracy), the unfiltered data are used when determiningthe direction of arrival (DoA), such that any damage/change in thephases of the values contained in the range Doppler maps X[k, l] causedby the neural network has no influence.

The training data for training the neural networks may be determined forexample by way of simulation. That is to say, digital radar data thatare overlaid with noise and interfering signals (interference) aregenerated by way of a signal model using simulation software that isexecuted for example on a computer. These data determined by way ofsimulation are fed to the neural networks and the resulting output datamay be compared with the “ideal” radar data (without noise andinterference). The weight factors in the kernels are adapted duringtraining of the neural networks such that the deviations of the filteredradar data from the ideal data are as small as possible. The deviationmay be evaluated for example by way of the least squares method. Thisevaluation function is usually referred to as object loss function.Training neural networks is known per se and is therefore not explainedin more detail here. By way of example, the neural network may betrained by way of the ADAM algorithm that is known per se.

Some of the example implementations described here are summarized below,it being pointed out that the summary below is not complete, but rathermerely an example summary. One example implementation relates to a radardevice with a radar transmitter and a radar receiver that may bearranged in one or in different radar chips (cf. FIG. 5 ). The radartransmitter is designed to output an RF transmission signal thatcomprises a plurality of frequency-modulated chirps (also referred to asfrequency-modulated pulses) (see FIG. 6 ). The radar receiver isdesigned to receive an RF radar signal (see FIG. 9 , graph (a)) and,based thereon, to generate a dataset (for example a range Dopplermatrix, see FIG. 9 , diagram (c)) in each case containing a particularnumber of digital values, wherein a dataset may be associated with achirp or a sequence of successive chirps. The radar device furthermorecomprises a neural network to which the dataset is fed and that isdesigned to filter the dataset in order to reduce an interfering signalcontained therein (see for example FIGS. 10A-10D, 15, and 16 ).According to one example implementation, the neural network may be aconvolutional neural network.

In one example implementation, the radar receiver is designed, based onthe RF radar signal, to generate a digital radar signal in the timedomain that comprises a plurality of signal segments that may beassociated with a sequence of frequency-modulated chirps (see FIG. 9 ,graphs (b) and (c)). The signal segments form a radar data array, andthe dataset is formed through two-dimensional Fourier transformation ofthe radar data array. The result of the two-dimensional Fouriertransformation is usually referred to as a range Doppler map.

In those example implementations in which the dataset of digital valueshas been determined by way of Fourier transformation, the digital valuesof the dataset are complex values that each have a real part and animaginary part (that is to say each complex value may be represented bya pair of real values). The neural network has an input layer with twoNN channels (see for example FIG. 15 ). A first channel of the two NNchannels delivers the real parts of the complex values of the dataset(for example of the range Doppler map) as output values, and a secondchannel of the two NN channels delivers the imaginary parts of thecomplex values of the dataset as output values. The output values of thetwo NN channels of the input layer are the input values of the followinglayer. The further layers (see for example FIG. 15 , layers L₁ to L_(S))of the neural network may each have two or more NN channels.

In one example implementation, the last layer (output layer) hasprecisely two NN channels, whereas the rest of the further layers havemore than two NN channels (sixteen NN channels in the example from FIG.15 ). A first NN channel of the output layer delivers the real parts asoutput values and a second NN channel of the output layer delivers theimaginary parts of complex values of a filtered dataset in whichinterfering signal components are reduced. In the example from FIG. 15 ,this filtered dataset represents the filtered range Doppler map{circumflex over (X)}[k, l]. Each NN channel of each further layer(following the input layer) of the neural network delivers a number ofreal output values (cf. equation 9, output values L_(S)[k, l, c]) thatcorresponds to the number of complex values of the dataset. That is tosay, the output values of each NN channel may be considered to be an N×Mmatrix/data array, wherein N×M also corresponds to the size of the rangeDoppler map.

In the example implementations described here, each layer of the neuralnetwork receives the output values of the NN channels of the respectiveprevious layer as input values. The layers of the neural network arereferred to as convolutional layers, wherein a convolution kernel is ineach case associated with the NN channels of the further layers. That isto say, a convolutional layer with sixteen NN channels also uses sixteenconvolution kernels. In this example, the output values of an NN channelof each of the further layers depend on a weighted sum of the inputvalues that are fed to the respective layer. Which and how many of theinput values are incorporated into the weighted sum depends on therespective convolution kernel.

A further example implementation relates to a method for a radar device,which method comprises the following: transmitting an RF transmissionsignal that comprises a plurality of frequency-modulated chirps (seeFIG. 1 , FIG. 2 and FIG. 6 ), and receiving an RF radar signal andgenerating a dataset containing in each case a particular number ofdigital values based on the received RF radar signal. A dataset may beassociated with a chirp or a sequence of successive chirps. The methodfurthermore comprises filtering the dataset by way of a neural networkto which the dataset is fed in order to reduce an interfering signalcontained therein (see for example FIGS. 10A-10D, FIG. 15 and FIG. 16 ).According to the example implementations described here, the neuralnetwork may be a convolutional neural network. The features mentionedabove with reference to the radar device also of course relate to themethod.

What is claimed is:
 1. A radar device, comprising: a radar receiver thatis designed to receive a radio-frequency (RF) radar signal associatedwith a plurality of frequency modulated chirps and, based thereon, togenerate a digital signal that comprises a plurality of signal segments;and a neural network having a plurality of layers each having one ormore neurons, wherein the plurality of signal segments are fed to aninput layer of the plurality of layers and wherein the plurality oflayers are designed to process the plurality of signal segments of thedigital signal, wherein an output layer of the plurality of layers, hasat least one neuron that delivers an output value that indicates whethera respective signal segment or a sample of the respective signal segmentis overlaid with an interfering signal originating from at least oneradar transmitter of at least one other radar device, wherein aconvolutional kernel is associated with one or more neural network (NN)channels of one or more other layers of the plurality of layers, andwherein the one or more other layers receive as input, output values ofthe one or more NN channels of a previous layer.
 2. The radar device asclaimed in claim 1, further comprising: a radar transmitter that isdesigned to generate an RF transmission signal that comprises aplurality of frequency-modulated pulses, wherein each of the pluralityof signal segments of the digital signal is associated with a respectivefrequency-modulated pulse of the plurality of frequency-modulatedpulses, and wherein the plurality of signal segments are in a timedomain or in a frequency domain.
 3. The radar device as claimed in claim1, wherein the output layer of the neural network is designed to deliveran output value that indicates whether the respective signal segment isoverlaid with a frequency-modulated interfering signal from an externalfrequency-modulated radar transmitter.
 4. The radar device as claimed inclaim 1, wherein the output layer has a neuron that indicates whetherone or more of the plurality of signal segments fed to the input layercontains an interfering signal.
 5. The radar device as claimed in claim1, further comprising: a computing unit that is designed to detect aradar target based on the digital signal, wherein one or more of theplurality of signal segments for which the at least one neuron of theoutput layer of the neural network indicates overlaying of aninterfering signal remain unconsidered.
 6. The radar device as claimedin claim 1, wherein the output layer has a plurality of neurons that areeach associated with a sample of the respective signal segment andwherein each of the plurality of neurons indicates whether the sampleassociated therewith contains an interfering signal.
 7. The radar deviceof claim 1, wherein the output values of the one or more NN channels ofthe one or more other layers are based on a weighted sum of input thatis provided to the respective layer, and wherein the input that isprovided to the respective layer is based on a respective convolutionkernel.
 8. A radar device, comprising: a radar transmitter that isdesigned to output a radio-frequency (RF) transmission signal thatcomprises a plurality of frequency-modulated chirps; a radar receiverthat is designed to receive an RF radar signal associated with aplurality of frequency modulated chirps and, based thereon, to generatea dataset containing a particular number of digital values, wherein adataset is associated with a chirp or a sequence of successive chirps;and a neural network to which the dataset is fed and that is designed tofilter the dataset in order to reduce an interfering signal originatingfrom at least one radar transmitter of at least one other radar devicecontained therein, wherein the neural network is a convolutional neuralnetwork with a plurality of layers, wherein a convolutional kernel isassociated with one or more neural network (NN) channels of a pluralityof further layers of the plurality of layers, and wherein the pluralityof further layers receive as input, output values of the one or more NNchannels of a previous layer.
 9. The radar device as claimed in claim 8,wherein the radar receiver is designed, based on the RF radar signal, togenerate a digital radar signal in a time domain that comprises aplurality of signal segments that are associated with a sequence offrequency-modulated chirps, and wherein the plurality of signal segmentsrepresent a radar data array and the dataset is formed throughtwo-dimensional Fourier transformation of the radar data array.
 10. Theradar device as claimed in claim 8, wherein the radar device isdesigned, based on the RF radar signal, to generate a digital radarsignal in a time domain that comprises a plurality of signal segmentsthat are associated with a sequence of frequency-modulated chirps, andbased on the digital radar signal, to calculate a range Doppler map thatforms the dataset of digital values.
 11. The radar device as claimed inclaim 10, wherein the dataset of digital values represents a dataset ofcomplex values; wherein the convolutional neural network has an inputlayer with two NN channels, wherein a first channel of the two NNchannels is to deliver real parts of the complex values of the datasetas output values, and wherein a second channel of the two NN channels isto deliver imaginary parts of the complex values of the dataset asoutput values.
 12. The radar device as claimed in claim 11, wherein eachof the plurality of further layers is associated with two or more NNchannels.
 13. The radar device as claimed in claim 12, wherein a lastlayer of the plurality of further layers forms an output layer that hastwo NN channels, and wherein a first channel of the two NN channels ofthe output layer is to deliver real parts as output values and a secondchannel of the two NN channels of the output layer is to deliverimaginary parts of complex values of a filtered dataset in whichinterfering signal components are reduced as output values.
 14. Theradar device as claimed in claim 13, wherein the output layer hasprecisely two NN channels and each of the rest of the plurality offurther layers has more than two NN channels.
 15. The radar device asclaimed in claim 12, wherein each NN channel of each of the plurality offurther layers of the neural network is to deliver a number of realoutput values that corresponds to a number of complex values of thedataset.
 16. The radar device as claimed in claim 8, wherein outputvalues of an NN channel of each respective layer of the plurality offurther layers depends on a weighted sum of input values that are fed tothe respective layer.
 17. A method for a radar device, comprising:transmitting a radio-frequency (RF) transmission signal that comprises aplurality of frequency-modulated chirps; receiving an RF radar signalassociated with a plurality of frequency modulated chirps, andgenerating a dataset containing a particular number of digital valuesbased on the RF radar signal, wherein a dataset is associated with achirp or a sequence of successive chirps; and filtering the datasetusing a neural network to which the dataset is fed in order to reduce aninterfering signal originating from at least one radar transmitter of atleast one other radar device contained therein, wherein the neuralnetwork is a convolutional neural network with a plurality of layers,wherein a convolutional kernel is associated with one or more neuralnetwork (NN) channels of one or more other layers of the plurality oflayers, and wherein the one or more other layers receive as input,output values of the NN channels of a previous layer.
 18. The method asclaimed in claim 17, wherein generating the dataset comprises:generating a digital radar signal in a time domain based on the RF radarsignal, wherein the digital radar signal comprises a plurality of signalsegments that are associated with a sequence of frequency-modulatedchirps, wherein the plurality of signal segments represent a radar dataarray; and forming the dataset through two-dimensional Fouriertransformation of the radar data array.
 19. The method as claimed inclaim 18, wherein the dataset of digital values is a dataset of complexvalues; wherein the neural network has an input layer with two NNchannels, wherein a first channel of the two NN channels delivers realparts of the complex values of the dataset as output values, and whereina second channel of the two NN channels delivers imaginary parts of thecomplex values of the dataset as output values.
 20. The method asclaimed in claim 17, wherein an output layer of the plurality of layershas at least one neuron that delivers an output value that indicateswhether a respective signal segment or a sample, associated with theneuron, of the respective signal segment is overlaid with an interferingsignal.